OpenRaiser/Intern-Atlas huggingface dataset:
A Deep Learning Approach to Anomaly Detection in High-Frequency Trading Data
Venue: arXiv cs.LG 2025
Year: 2025

This paper proposes an algorithm based on a staged sliding window Transformer architecture to detect abnormal behaviors in the microstructure of the foreign exchange market, focusing on high-frequency EUR/USD trading data. The method captures multi-scale temporal features through a staged sliding window, extracts global and local dependencies by combining the self-attention mechanism and weighted attention mechanism of the Transformer, and uses a classifier to identify abnormal events. Experimental results on a real high-frequency dataset containing order book depth, spread, and trading volume show that the proposed method significantly outperforms traditional machine learning (such as decision trees and random forests) and deep learning methods (such as MLP, CNN, RNN, LSTM) in terms of accuracy (0.93), F1-Score (0.91), and AUC-ROC (0.95). Ablation experiments verify the contribution of each component, and the visualization of order book depth and anomaly detection further reveals the effectiveness of the model under complex market dynamics. Despite the false positive problem, the model still provides important support for market supervision. In the future, noise processing can be optimized and extended to other markets to improve generalization and real-time performance.
Diffusion models for probabilistic precipitation generation from atmospheric variables
Venue: arXiv cs.LG 2025
Year: 2025

Improving the representation of precipitation in Earth system models (ESMs) is critical for assessing the impacts of climate change and especially of extreme events like floods and droughts. In existing ESMs, precipitation is not resolved explicitly, but represented by parameterizations. These typically rely on resolving approximated but computationally expensive column-based physics, not accounting for interactions between locations. They struggle to capture fine-scale precipitation processes and introduce significant biases. We present a novel approach, based on generative machine learning, which integrates a conditional diffusion model with a UNet architecture to generate accurate, high-resolution (0.25°) global daily precipitation fields from a small set of prognostic atmospheric variables. Unlike traditional parameterizations, our framework efficiently produces ensemble predictions, capturing uncertainties in precipitation, and does not require fine-tuning by hand. We train our model on the ERA5 reanalysis and present a method that allows us to apply it to arbitrary ESM data, enabling fast generation of probabilistic forecasts and climate scenarios. By leveraging interactions between global prognostic variables, our approach provides an alternative parameterization scheme that mitigates biases present in the ESM precipitation while maintaining consistency with its large-scale (annual) trends. This work demonstrates that complex precipitation patterns can be learned directly from large-scale atmospheric variables, offering a computationally efficient alternative to conventional schemes.
SHIFT SNARE: Uncovering Secret Keys in FALCON via Single-Trace Analysis
Venue: arXiv cs.CR 2025
Year: 2025

This paper presents a novel singletrace sidechannel attack on FALCON a latticebased postquantum digital signature protocol recently approved for standardization by NIST We target the discrete Gaussian sampling operation within FALCONs key generation scheme and demonstrate that a single power trace is sufficient to mount a successful attack Notably negating the results of a 63bit rightshift operation on 64bit secret values leaks critical information about the assignment of 1 versus 0 to intermediate coefficients during sampling These leaks enable full recovery of the secret key We demonstrate a groundup approach to the attack on an ARM CortexM4 microcontroller executing both the reference and optimized implementations from FALCONs NIST round 3 software package We successfully recovered all of the secret polynomials in FALCON We further quantify the attackers success rate using a univariate Gaussian template model providing generalizable guarantees Statistical analysis with over 500000 tests reveals a percoefficient success rate of 999999999478 and a fullkey recovery rate of 9999994654 for FALCON512 We verify that this vulnerability is present in all implementations included in FALCONs NIST submission package This highlights the vulnerability of current software implementations to singletrace attacks and underscores the urgent need for singletrace resilient software in embedded systems
Shaping the Future of VR Hand Interactions: Lessons Learned from Modern Methods
Venue: arXiv cs.HC 2025
Year: 2025

In virtual reality, it is widely assumed that increased realism in hand-object interactions enhances user immersion and overall experience. However, recent studies challenge this assumption, suggesting that faithfully replicating real-world physics and visuals is not always necessary for improved usability or immersion. This has led to ambiguity for developers when choosing optimal hand interaction methods for different applications. Currently, there is a lack of comprehensive research to resolve this issue. This study aims to fill this gap by evaluating three contemporary VR hand interaction methods-Attachment, Penetration, and Torque-across two distinct task scenarios: simple manipulation tasks and more complex, precision-driven tasks. By examining key technical features, we identify the strengths and limitations of each method and propose development guidelines for future advancements. Our findings reveal that while Attachment, with its simplified control mechanisms, is well-suited for commercial applications, Penetration and Torque show promise for next-generation interactions. The insights gained from our study provide practical guidance for developers and researchers seeking to balance realism, usability, and user satisfaction in VR environments.
Integrated LLM-Based Intrusion Detection with Secure Slicing xApp for Securing O-RAN-Enabled Wireless Network Deployments
Venue: arXiv cs.CR 2025
Year: 2025

The Open Radio Access Network (O-RAN) architecture is reshaping telecommunications by promoting openness, flexibility, and intelligent closed-loop optimization. By decoupling hardware and software and enabling multi-vendor deployments, O-RAN reduces costs, enhances performance, and allows rapid adaptation to new technologies. A key innovation is intelligent network slicing, which partitions networks into isolated slices tailored for specific use cases or quality of service requirements. The RAN Intelligent Controller further optimizes resource allocation, ensuring efficient utilization and improved service quality for user equipment (UEs). However, the modular and dynamic nature of O-RAN expands the threat surface, necessitating advanced security measures to maintain network integrity, confidentiality, and availability. Intrusion detection systems have become essential for identifying and mitigating attacks. This research explores using large language models (LLMs) to generate security recommendations based on the temporal traffic patterns of connected UEs. The paper introduces an LLM-driven intrusion detection framework and demonstrates its efficacy through experimental deployments, comparing non fine-tuned and fine-tuned models for task-specific accuracy.
Robust Transmission Design for Active RIS-Aided Systems
Venue: arXiv cs.IT 2025
Year: 2025

Different from conventional passive reconfigurable intelligent surfaces (RISs), incident signals and thermal noise can be amplified at active RISs. By exploiting the amplifying capability of active RISs, noticeable performance improvement can be expected when precise channel state information (CSI) is available. Since obtaining perfect CSI related to an RIS is difficult in practice, a robust transmission design is proposed in this paper to tackle the channel uncertainty issue, which will be more severe for active RIS-aided systems. To account for the worst-case scenario, the minimum achievable rate of each user is derived under a statistical CSI error model. Subsequently, an optimization problem is formulated to maximize the sum of the minimum achievable rate. Since the objective function is non-concave, the formulated problem is transformed into a tractable lower bound maximization problem, which is solved using an alternating optimization method. Numerical results show that the proposed robust design outperforms a baseline scheme that only exploits estimated CSI.
AP-CAP: Advancing High-Quality Data Synthesis for Animal Pose Estimation via a Controllable Image Generation Pipeline
Venue: arXiv cs.CV 2025
Year: 2025

The task of 2D animal pose estimation plays a crucial role in advancing deep learning applications in animal behavior analysis and ecological research. Despite notable progress in some existing approaches, our study reveals that the scarcity of high-quality datasets remains a significant bottleneck, limiting the full potential of current methods. To address this challenge, we propose a novel Controllable Image Generation Pipeline for synthesizing animal pose estimation data, termed AP-CAP. Within this pipeline, we introduce a Multi-Modal Animal Image Generation Model capable of producing images with expected poses. To enhance the quality and diversity of the generated data, we further propose three innovative strategies: (1) Modality-Fusion-Based Animal Image Synthesis Strategy to integrate multi-source appearance representations, (2) Pose-Adjustment-Based Animal Image Synthesis Strategy to dynamically capture diverse pose variations, and (3) Caption-Enhancement-Based Animal Image Synthesis Strategy to enrich visual semantic understanding. Leveraging the proposed model and strategies, we create the MPCH Dataset (Modality-Pose-Caption Hybrid), the first hybrid dataset that innovatively combines synthetic and real data, establishing the largest-scale multi-source heterogeneous benchmark repository for animal pose estimation to date. Extensive experiments demonstrate the superiority of our method in improving both the performance and generalization capability of animal pose estimators.
A Theory of Machine Understanding via the Minimum Description Length Principle
Venue: arXiv cs.LG 2025
Year: 2025

Deep neural networks trained through end-to-end learning have achieved remarkable success across various domains in the past decade. However, the end-to-end learning strategy, originally designed to minimize predictive loss in a black-box manner, faces two fundamental limitations: the struggle to form explainable representations in a self-supervised manner, and the inability to compress information rigorously following the Minimum Description Length (MDL) principle. These two limitations point to a deeper issue: an end-to-end learning model is not able to "understand" what it learns. In this paper, we establish a novel theory connecting these two limitations. We design the Spectrum VAE, a novel deep learning architecture whose minimum description length (MDL) can be rigorously evaluated. Then, we introduce the concept of latent dimension combinations, or what we term spiking patterns, and demonstrate that the observed spiking patterns should be as few as possible based on the training data in order for the Spectrum VAE to achieve the MDL. Finally, our theory demonstrates that when the MDL is achieved with respect to the given data distribution, the Spectrum VAE will naturally produce explainable latent representations of the data. In other words, explainable representations--or "understanding"--can emerge in a self-supervised manner simply by making the deep network obey the MDL principle. In our opinion, this also implies a deeper insight: To understand is to compress. At its core, our theory advocates for a shift in the training objective of deep networks: not only to minimize predictive loss, but also to minimize the description length regarding the given data. That is, a deep network should not only learn, but also understand what it learns. This work is entirely theoretical and aims to inspire future research toward self-supervised, explainable AI grounded in the MDL principle.
Adaptive Low Light Enhancement via Joint Global-Local Illumination Adjustment
Venue: arXiv cs.CV 2025
Year: 2025

Images captured under real-world low-light conditions face significant challenges due to uneven ambient lighting, making it difficult for existing end-to-end methods to enhance images with a large dynamic range to normal exposure levels. To address the above issue, we propose a novel brightness-adaptive enhancement framework designed to tackle the challenge of local exposure inconsistencies in real-world low-light images. Specifically, our proposed framework comprises two components: the Local Contrast Enhancement Network (LCEN) and the Global Illumination Guidance Network (GIGN). We introduce an early stopping mechanism in the LCEN and design a local discriminative module, which adaptively perceives the contrast of different areas in the image to control the premature termination of the enhancement process for patches with varying exposure levels. Additionally, within the GIGN, we design a global attention guidance module that effectively models global illumination by capturing long-range dependencies and contextual information within the image, which guides the local contrast enhancement network to significantly improve brightness across different regions. Finally, in order to coordinate the LCEN and GIGN, we design a novel training strategy to facilitate the training process. Experiments on multiple datasets demonstrate that our method achieves superior quantitative and qualitative results compared to state-of-the-art algorithms.
Performance Evaluation of Scheduling Scheme in O-RAN 5G Network using NS-3
Venue: arXiv cs.NI 2025
Year: 2025

The integration of Open Radio Access Network (O-RAN) principles into 5G networks introduces a paradigm shift in how radio resources are managed and optimized. O-RAN's open architecture enables the deployment of intelligent applications (xApps) that can dynamically adapt to varying network conditions and user demands. In this paper, we present radio resource scheduling schemes -- a possible O-RAN-compliant xApp can be designed. This xApp facilitates the implementation of customized scheduling strategies, tailored to meet the diverse Quality-of-Service (QoS) requirements of emerging 5G use cases, such as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). We have tested the implemented scheduling schemes within an ns-3 simulation environment, integrated with the O-RAN framework. The evaluation includes the implementation of the Max-Throughput (MT) scheduling policy -- which prioritizes resource allocation based on optimal channel conditions, the Proportional-Fair (PF) scheduling policy -- which balances fairness with throughput, and compared with the default Round Robin (RR) scheduler. In addition, the implemented scheduling schemes support dynamic Time Division Duplex (TDD), allowing flexible configuration of Downlink (DL) and Uplink (UL) switching for bidirectional transmissions, ensuring efficient resource utilization across various scenarios. The results demonstrate resource allocation's effectiveness under MT and PF scheduling policies. To assess the efficiency of this resource allocation, we analyzed the Modulation Coding Scheme (MCS), the number of symbols, and Transmission Time Intervals (TTIs) allocated per user, and compared them with the throughput achieved. The analysis revealed a consistent relationship between these factors and the observed throughput.
Enhancing Fundus Image-based Glaucoma Screening via Dynamic Global-Local Feature Integration
Venue: arXiv cs.CV 2025
Year: 2025

With the advancements in medical artificial intelligence (AI), fundus image classifiers are increasingly being applied to assist in ophthalmic diagnosis. While existing classification models have achieved high accuracy on specific fundus datasets, they struggle to address real-world challenges such as variations in image quality across different imaging devices, discrepancies between training and testing images across different racial groups, and the uncertain boundaries due to the characteristics of glaucomatous cases. In this study, we aim to address the above challenges posed by image variations by highlighting the importance of incorporating comprehensive fundus image information, including the optic cup (OC) and optic disc (OD) regions, and other key image patches. Specifically, we propose a self-adaptive attention window that autonomously determines optimal boundaries for enhanced feature extraction. Additionally, we introduce a multi-head attention mechanism to effectively fuse global and local features via feature linear readout, improving the model's discriminative capability. Experimental results demonstrate that our method achieves superior accuracy and robustness in glaucoma classification.
Egocentric Conformal Prediction for Safe and Efficient Navigation in Dynamic Cluttered Environments
Venue: arXiv cs.RO 2025
Year: 2025

Conformal prediction (CP) has emerged as a powerful tool in robotics and control, thanks to its ability to calibrate complex, data-driven models with formal guarantees. However, in robot navigation tasks, existing CP-based methods often decouple prediction from control, evaluating models without considering whether prediction errors actually compromise safety. Consequently, ego-vehicles may become overly conservative or even immobilized when all potential trajectories appear infeasible. To address this issue, we propose a novel CP-based navigation framework that responds exclusively to safety-critical prediction errors. Our approach introduces egocentric score functions that quantify how much closer obstacles are to a candidate vehicle position than anticipated. These score functions are then integrated into a model predictive control scheme, wherein each candidate state is individually evaluated for safety. Combined with an adaptive CP mechanism, our framework dynamically adjusts to changes in obstacle motion without resorting to unnecessary conservatism. Theoretical analyses indicate that our method outperforms existing CP-based approaches in terms of cost-efficiency while maintaining the desired safety levels, as further validated through experiments on real-world datasets featuring densely populated pedestrian environments.
MetaLoRA: Tensor-Enhanced Adaptive Low-Rank Fine-tuning
Venue: arXiv cs.LG 2025
Year: 2025

There has been a significant increase in the deployment of neural network models, presenting substantial challenges in model adaptation and fine-tuning. Efficient adaptation is crucial in maintaining model performance across diverse tasks and domains. While Low-Rank Adaptation (LoRA) has emerged as a promising parameter-efficient fine-tuning method, its fixed parameter nature limits its ability to handle dynamic task requirements effectively. Adapting models to new tasks can be challenging due to the need for extensive fine-tuning. Current LoRA variants primarily focus on general parameter reduction while overlooking the importance of dynamic parameter adjustment and meta-learning capabilities. Moreover, existing approaches mainly address static adaptations, neglecting the potential benefits of task-aware parameter generation in handling diverse task distributions. To address these limitations, this Ph.D. research proposes a LoRA generation approach to model task relationships and introduces MetaLoRA, a novel parameter-efficient adaptation framework incorporating meta-learning principles. This work develops a comprehensive architecture that integrates meta-parameter generation with adaptive low-rank decomposition, enabling efficient handling of both task-specific and task-agnostic features. MetaLoRA accurately captures task patterns by incorporating meta-learning mechanisms and dynamic parameter adjustment strategies. To our knowledge, this research represents the first attempt to provide a meta-learning enhanced LoRA variant, offering improved adaptation capability while maintaining computational efficiency in model fine-tuning.
Bayesian Network Structural Consensus via Greedy Min-Cut Analysis
Venue: arXiv cs.LG 2025
Year: 2025

This paper presents the Min-Cut Bayesian Network Consensus (MCBNC) algorithm, a greedy method for structural consensus of Bayesian Networks (BNs), with applications in federated learning and model aggregation. MCBNC prunes weak edges from an initial unrestricted fusion using a structural score based on min-cut analysis, integrated into a modified Backward Equivalence Search (BES) phase of the Greedy Equivalence Search (GES) algorithm. The score quantifies edge support across input networks and is computed using max-flow. Unlike methods with fixed treewidth bounds, MCBNC introduces a pruning threshold $θ$ that can be selected post hoc using only structural information. Experiments on real-world BNs show that MCBNC yields sparser, more accurate consensus structures than both canonical fusion and the input networks. The method is scalable, data-agnostic, and well-suited for distributed or federated scenarios.
Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection
Venue: arXiv cs.LG 2025
Year: 2025

To develop a trustworthy AI system, which aim to identify the input regions that most influence the models decisions. The primary task of existing attribution methods lies in efficiently and accurately identifying the relationships among input-prediction interactions. Particularly when the input data is discrete, such as images, analyzing the relationship between inputs and outputs poses a significant challenge due to the combinatorial explosion. In this paper, we propose a novel and efficient black-box attribution mechanism, LiMA (Less input is More faithful for Attribution), which reformulates the attribution of important regions as an optimization problem for submodular subset selection. First, to accurately assess interactions, we design a submodular function that quantifies subset importance and effectively captures their impact on decision outcomes. Then, efficiently ranking input sub-regions by their importance for attribution, we improve optimization efficiency through a novel bidirectional greedy search algorithm. LiMA identifies both the most and least important samples while ensuring an optimal attribution boundary that minimizes errors. Extensive experiments on eight foundation models demonstrate that our method provides faithful interpretations with fewer regions and exhibits strong generalization, shows an average improvement of 36.3% in Insertion and 39.6% in Deletion. Our method also outperforms the naive greedy search in attribution efficiency, being 1.6 times faster. Furthermore, when explaining the reasons behind model prediction errors, the average highest confidence achieved by our method is, on average, 86.1% higher than that of state-of-the-art attribution algorithms. The code is available at https://github.com/RuoyuChen10/LIMA.